Theory Revision and Related Problems in Learning Theory

理论修正是对给定的、大致正确的规则的修正，也称为概念或理论。 这个问题经常出现在机器学习中，例如，当专家系统的初始输出不正确时，以及当机器学习问题太大或太复杂而无法从头开始解决时，需要一个近似正确的规则来启动学习过程。 已经有相当多的理论修正系统的特设建设，但理论是知之甚少。 本研究探讨了有效理论修正的基本数学可能性和局限性。 我们希望，作为本研究的结果，计算学习理论中的理论修正将成为学习情境研究的一般框架，其中大量的初始信息是可用的或必要的。 特别是，PI调查以下领域：扩展他们以前的工作命题逻辑理论revisionwith查询，关系到证书的复杂性和有效的学习，谓词logicrepresentations的修订问题，以及学习和修订的范畴语法。

Theory revision is the correcting of a given, roughly correct rule,also known as a concept or theory. This problem arises frequently inmachine learning, for instance, when the initial output of an expert system is not correct, and when the machine learningproblem is too large or too complex to solve from scratch, and anapproximately correct rule is needed to jump-start the learningprocess. There has been considerable ad hoc building of theory revision systems, but the theory is poorly understood. This researchinvestigates fundamental mathematical possibilities and limitationsof efficient theory revision. It is hoped that as a result ofthis research, theory revision in computational learning theory willemerge as a general framework for the study of learning situationswhere a large amount of initial information is available ornecessary. In particular, the PIs investigate the following areas: extensionsof their previous work on propositional logic theory revisionwith queries, relations to certificate complexity andattribute-efficient learning, revision problems for predicate logicrepresentations, and both the learning and revising of categorial grammars.